DJL 0.4.0 brings PyTorch and TensorFlow 2.0 inference support. Now you can use these engines directly from DJL with minimum code changes.

Note: TensorFlow 2.0 currently is in PoC stage, users will have to build from source to use it. We expect TF Engine finish in the future releases.

Key Features

• Training improvement
  • Add InputStreamTranslator
• Model Zoo improvement
  • Add LocalZooProvider
  • Add ListModels API
• PyTorch Engine support
  • Use the new ai.djl.pytorch:pytorch-native-auto dependency for automatic engine selection and a simpler build/installation process
  • 60+ methods supported
• PyTorch ModelZoo support
  • Image Classification models: ResNet18 and ResNet50
  • Object Detection model: SSD_ResNet50
• TensorFlow 2.0 Engine support
  • Support on Eager Execution for imperative mode
  • 30+ methods support
• TensorFlow ModelZoo support
  • Image Classification models: ResNet50, MobileNetV2

Breaking Changes

There are a few changes in API and ModelZoo packages to adapt to multi-engine support. Please follow our latest examples to update your code base from 0.3.0 to 0.4.0.

Known Issues

1. PyTorch engine doesn't fully support multithreaded inference. You may see random crashes. Single-threaded inference is not impacted. We expect to fix this issue in a future release.
2. We saw random crash on mac for “transfer Learning on CIFAR-10 Dataset” example on Jupyter Notebook. Command line all works.